Blind Image Restoration Algorithm Iteratively Using Wavelet Denoising and Total Variation Regularization

Zou  Zuo-Feng; Shui  Peng-Lang

doi:10.3724/SP.J.1146.2007.00817

Volume 30 Issue 12

Jan. 2011

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Zou Zuo-Feng, Shui Peng-Lang. Blind Image Restoration Algorithm Iteratively Using Wavelet Denoising and Total Variation Regularization[J]. Journal of Electronics & Information Technology, 2008, 30(12): 2912-2915. doi: 10.3724/SP.J.1146.2007.00817

Citation:

Zou Zuo-Feng, Shui Peng-Lang. Blind Image Restoration Algorithm Iteratively Using Wavelet Denoising and Total Variation Regularization[J]. Journal of Electronics & Information Technology, 2008, 30(12): 2912-2915. doi: 10.3724/SP.J.1146.2007.00817

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Blind Image Restoration Algorithm Iteratively Using Wavelet Denoising and Total Variation Regularization

doi: 10.3724/SP.J.1146.2007.00817

Zou Zuo-Feng^{①② 水鹏朗
,},
Shui Peng-Lang

Received Date: 2007-05-28
Rev Recd Date: 2007-10-08
Publish Date: 2008-12-19

Abstract

Abstract

Blind image restoration is to recover the original image form the observed degraded image with unknown the Point Spread Function (PSF). This paper proposes a blind image restoration algorithm iteratively using wavelet denoising and total variation regularization. The observation model is first divided into two mutually associated sub-models, and this representation converses the blind restoration into the two issues of image denoising and image restoration, which makes us to solve the problem by iteratively using image denoising and image restoration algorithms. The stage of the PSF identification uses Total Variation (TV) regularization and the stage of image restoration uses wavelet denoising and TV regularization. The experimental results show that the proposed algorithm achieves better performance than the existing algorithms.
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References(1)

References

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